【摘要】：在偏好決策問題中,由于不完備信息普遍存在于應用環(huán)境中,相關(guān)學者提出了多種優(yōu)勢關(guān)系來應對這一問題。同時,與之相對應的粗糙集模型也得到了拓展。但在實際應用環(huán)境中,仍然存在許多改進之處。本文通過對影響誤分類因素的分析,尋找不完備信息下粗糙集模型的改進點,從而提高粗糙集模型的分類性能,并將改進后的粗糙集模型應用到A公司數(shù)據(jù)分析中。實際應用表明,新的粗糙集模型分類性能更優(yōu)。以下是本文的主要工作:權(quán)重優(yōu)勢關(guān)系的定義。在廣義擴展優(yōu)勢關(guān)系的基礎之上,考慮了不同屬性重要度不同對分類的影響。提出了不完備信息系統(tǒng)中基于粗糙集的權(quán)重設定方法,引入一個閾值來對優(yōu)勢關(guān)系進行限定,并給出了閾值的選取方法。在此基礎上提出了權(quán)重優(yōu)勢關(guān)系及其粗糙集模型。通過實例的運算驗證,相對于廣義擴展優(yōu)勢關(guān)系而言權(quán)重優(yōu)勢關(guān)系使得粗糙集模型具有更好的分類性能�；叶葍�(yōu)勢關(guān)系的定義。通過分析造成優(yōu)勢二元關(guān)系不確定性的影響因素,以可比較信息的多少、條件屬性的權(quán)重大小、以及屬性之間差異程度為三個維度,構(gòu)建兩對象對比時確定性程度的灰度度量。定義了優(yōu)勢關(guān)系中差異系數(shù)以及灰度的概念,并以此為基礎建立灰度優(yōu)勢關(guān)系,并提出相應的粗糙集模型。通過理論證明以及實例運算,證明了灰度優(yōu)勢粗糙集模型的實用性。在工業(yè)項目數(shù)據(jù)中的應用分析。對現(xiàn)有的A公司工業(yè)項目數(shù)據(jù)進行分析、量化,應用前面提出的各屬性權(quán)重的設定方法和灰度優(yōu)勢關(guān)系的粗糙集模型對工業(yè)項目數(shù)據(jù)進行建模。并對其決策規(guī)則進行挖掘,為工業(yè)項目的選擇以及投資提供決策依據(jù)。
[Abstract]:In the problem of preference decision-making, because incomplete information exists widely in the application environment, relevant scholars have proposed a variety of advantages to deal with this problem. At the same time, the corresponding rough set model is extended. However, in the practical application environment, there are still many improvements. In this paper, through the analysis of the factors affecting the misclassification, the improved points of rough set model under incomplete information are found to improve the classification performance of the rough set model, and the improved rough set model is applied to the data analysis of Company A. The practical application shows that the classification performance of the new rough set model is better. The following is the main work of this paper: the definition of weight superiority relationship. On the basis of generalized extended dominance relation, the influence of different attribute importance on classification is considered. The weight setting method based on rough set in incomplete information system is proposed. A threshold value is introduced to limit the superiority relation, and the selection method of threshold is given. On this basis, the weight dominance relation and its rough set model are proposed. The calculation results show that the rough set model has better classification performance than the generalized extended dominance relation. Definition of gray-scale dominance relationship. By analyzing the influencing factors of the uncertainty of the dominant binary relationship, the gray scale measurement of the certainty degree when the two objects are compared is constructed according to the number of comparable information, the weight size of the conditional attribute and the degree of difference between the attributes in order to construct the gray scale measure of the degree of certainty when the two objects are compared. In this paper, the concept of difference coefficient and gray scale in superiority relation is defined, and based on it, the grey superiority relation is established, and the corresponding rough set model is put forward. The practicability of the gray-scale dominant rough set model is proved by theoretical proof and practical operation. Application analysis in industrial project data. This paper analyzes and quantifies the existing industrial project data of Company A, and models the industrial project data by using the method of setting the weight of each attribute and the rough set model of gray-level superiority relation. And mining its decision-making rules to provide the decision-making basis for the choice of industrial projects and investment.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F224;F272

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